Find open-source science resources

Python toolkit for fine-tuning geospatial foundation models

Python package for segmenting geospatial data with the Segment Anything Model (SAM), enabling zero-shot object segmentation in satellite and aerial imagery for remote sensing and Earth observation (MIT, 4k+ stars)

Microsoft's foundation model for the Earth system supporting weather, air pollution, and ocean wave forecasting at multiple resolutions, trained on 1M+ hours of diverse atmospheric data (Nature 2025)

Microsoft's AI-powered geospatial Earth science application for natural-language exploration, visualization, and analysis of 130+ satellite collections, with STAC integration, multi-agent backend, MCP server, and deployable React/FastAPI stack (MIT, 2025)

Google Research's hybrid ML/physics atmospheric model combining learned dynamics with physical constraints, outperforming traditional models on 2-15 day forecasts and 40-year climate simulation, developed with ECMWF (Nature 2024)

ECMWF's open-source machine-learning Earth system model developed by the WeatherGenerator Consortium with NVIDIA, trained on reanalyses, forecast data, and diverse observations across atmosphere, ocean, and land to provide a robust multi-scale model of Earth system dynamics; the first released version (v0.1, trained on ERA5) demonstrates global probabilistic forecasting skill on par with established AI models, with open training framework and config-driven multi-dataset ingestion pipeline (Apache 2.0)

Family of operational-quality open weather models from DeepMind and Google Research, including WeatherNext Graph (deterministic GNN medium-range forecasting, published as GraphCast), WeatherNext Gen (diffusion ensemble, published as GenCast), WeatherNext 2 (state-of-the-art global medium-range and cyclone forecasting skillful beyond 15 days, operational at 0.25° resolution), and WeatherNext Cyclones (breakthrough tropical cyclone track forecasting, Nature 2026); official code and open weights (Apache 2.0, 7.6K+ stars)

World's first fully open, accelerated weather AI software stack with Medium Range forecasting and Nowcasting models using generative AI (January 2026)

High-level open-source geospatial AI package for satellite/aerial imagery analysis, model training, inference, interactive visualization, and QGIS integration, bridging PyTorch/Transformers with remote sensing workflows (MIT, 2026)

Universal graph neural network framework for global-to-regional Earth system forecasting, combining multi-grid theory with a dynamic-system perspective to build multi-scale graphs that densify target regions for local high-frequency features; adaptive message passing with dynamic gating units is theoretically proven to act as high-pass filtering against over-smoothing, and a neural nested-grid method mitigates boundary information loss in high-resolution regional forecasts; extended to causally-coupled ocean-atmosphere cross-sphere modeling with strong extreme-event prediction, releasing inference/training code, pretrained weights, and preprocessed data (Renmin University & PolyU, 213+ stars, MIT License)

Google DeepMind's diffusion-based ensemble weather forecasting model at 0.25° resolution, outperforming ECMWF ENS on 97.2% of targets up to 15 days ahead, with open-source code and weights (Nature 2024)

ECMWF's unified framework and command-line tool to run AI-based weather forecasting models (GraphCast, Aurora, Pangu, NeuralGCM, FourCastNet) with operational ECMWF data infrastructure, enabling standardized inference and benchmarking across state-of-the-art meteorological AI systems (ECMWF, 576+ stars)

Next-generation benchmark for data-driven global weather models with standardized evaluation framework and curated datasets for ML forecasting (Google Research, 2024)

Fudan University's cascade machine learning forecasting system for 15-day global weather prediction, employing a 3D Earth-specific transformer with hard-constraint techniques to achieve state-of-the-art accuracy against traditional NWP and AI baselines

Surprisingly simple and efficient frontier probabilistic weather forecaster built on a standard U-Net trained with deterministic MAE pre-training followed by short CRPS fine-tuning via Monte Carlo Dropout, matching or exceeding the probabilistic skill of GenCast and IFS ENS at 1.5° resolution with >10× less training compute than leading CRPS models and >10× lower inference latency than diffusion models; trains in under 12 H200 GPU-days and generates a 15-day ensemble forecast in 3 seconds, with official code and pretrained checkpoints (UCLA, 41+ stars, Apache 2.0)

LLM agent framework for Earth Observation with 104 specialized tools across 5 functional kits

Shanghai AI Lab's deep learning-based global weather forecasting model pushing skillful forecasts beyond 10 days lead, with open-source inference code and pretrained ONNX model weights (arXiv 2023)

Curated list of large weather models for AI Earth science

First end-to-end data-driven weather prediction system learning directly from raw, heterogeneous Earth observations rather than physics-based reanalysis, producing both global gridded and arbitrary station forecasts; releases model weights, training code, and an ML-ready observational dataset (2007-2019) for building future end-to-end weather models (221+ stars, CC0-1.0)

Minimal-modification vision transformer for skillful and reliable medium-range weather forecasting, introducing weather-specific patch embedding, randomized dynamics forecasting over varying time intervals, and pressure-weighted loss; competitive at short range and outperforming prior methods beyond 7 days on WeatherBench 2 with orders-of-magnitude less training data and compute, with favorable scaling in model size and training tokens (MIT License)

Physics-AI hybrid modeling for fine-grained weather forecasting (NeurIPS'24)

Climate data benchmark for ML models

Huawei's 3D high-resolution global weather forecast model at 0.25° resolution, first AI method to comprehensively outperform traditional NWP across all variables and lead times, integrated into ECMWF operational forecasts (Nature 2023)

Weather prediction benchmark

First foundation model for weather and climate by Microsoft, Vision Transformer-based architecture trained on heterogeneous datasets (ICML 2023)